MLOps: the future of Machine Learning Operations in the digital transformation

Digital transformation is redefining business operations, leading organisations into an era marked by automation and advanced data analysis. In this context, Artificial Intelligence (AI) and Machine Learning (ML) are key players in the transformation, enabling companies to innovate and adapt to an ever-changing market.

With the growing use of AI in business, MLOps (Machine Learning Operations) is becoming increasingly crucial to business success by enabling the development, deployment and monitoring of Machine Learning models in a more agile and precise way.

What is MLOps?

MLOps is an approach that combines DevOps practices with the specific needs of developing and operating Machine Learning models. By integrating development, testing, deployment and monitoring into a continuous cycle, MLOps seeks to accelerate the delivery of ML models into production, guaranteeing efficiency and reliability.

MLOps has emerged as a facilitator, making it possible to implement AI and ML on a large scale.

Unlike traditional development practices, MLOps adopts a collaborative approach involving multidisciplinary teams, including data scientists, software engineers and IT professionals.

Challenges that MLOps is overcoming

MLOps has come to streamline and overcome the challenges faced by the Machine Learning field and its professionals:

  • Data quality and availability: ML models depended on data, but this was often disorganised, incomplete or out of date. ML professionals had to ensure that there was sufficient, clean, consistent and relevant data for their models.
  • Complexity and diversity of models: ML models were diverse and dynamic, with different techniques, frameworks and applications. This required selecting models specific to the problem and managing their complexity and variability.
  • Model implementation and maintenance: ML models were not static, they constantly changed and evolved. This required implementing the models in production environments - guaranteeing their reliability, scalability and security - and monitoring and updating them as necessary.

Why are MLOps becoming essential for companies?

In recent years, data has grown exponentially. According to the International Data Corporation (IDC), by 2025 the volume of data generated worldwide will reach 175 zettabytes, or 175 billion terabytes. To deal with these massive volumes, it is crucial to have efficient solutions to process and extract value from this information. MLOps has emerged as a facilitator, making it possible to implement AI and ML on a large scale.

MLOps is here to change the paradigm, offering advantages that include streamlining and automating the Machine Learning process

Artificial Intelligence (AI) and Machine Learning (ML) are fundamental for organisations in various areas, such as health, finance, logistics and marketing, as they allow complex processes to be automated and large volumes of data to be analysed in real time. MLOps streamlines complex Machine Learning processes and creates continuous delivery cycles.

MLOps is not a trend, but a necessity for the future of ML. As ML becomes more pervasive and impactful, MLOps becomes more essential and valuable. MLOps can help ML professionals create and deliver models that can solve real-world problems and generate value for their customers.

Benefits of MLOps

MLOps is here to change the paradigm, offering advantages that include streamlining and automating the Machine Learning process, from development to production. Inspired by DevOps, MLOps bridges the gap between developers and operations teams. It helps Machine Learning professionals overcome process challenges, enabling faster, more efficient and sustainable delivery of Machine Learning models. This includes optimised data management, agile model development and testing, simplified deployment of models into production and continuous monitoring to ensure model performance and quality.

The main advantages of MLOps include:

  1. Agility: Reduces the time needed to put models into production, allowing organisations to respond more quickly to market changes.
  2. Collaboration: Facilitates integration between data scientists, engineers and other professionals, promoting a collaborative environment for developing solutions.
  3. Scalability: Allows organisations to scale their ML operations efficiently, adapting to growing demands.
  4. Reliability: Through continuous testing and monitoring, MLOps guarantees the quality and stability of models in production.

Promote digital transformation with MLOps from Izertis

At Izertis, we are a leading technology consultancy committed to driving innovation and the digital transformation of organisations. With our advanced technological capabilities, we offer a diversified portfolio that ranges from Artificial Intelligence and Machine Learning to DevOps, Cloud and Cybersecurity.

In the last year, full of automation and AI news, we had the opportunity to take part in the Code Europe conference, where we addressed the importance of Machine Learning in open source and shared our experience in MLOps. We took to the stage as speakers, discussing the latest trends and practices in Machine Learning and MLOps, and explored our contribution to the DVC VSCode extension experimentation tool.